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Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10113))

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Abstract

Radial lens distortion often exists in images taken by common cameras, which violates the assumption of pinhole camera model. Estimating the radial lens distortion of an image is an important preprocessing step for many vision applications. This paper intends to employ CNNs (Convolutional Neural Networks), to achieve radial distortion correction. However, the main issue hinder its progress is the scarcity of training data with radial distortion annotations. Inspired by the growing availability of image dataset with non-radial distortion, we propose a framework to address the issue by synthesizing images with radial distortion for CNNs. We believe that a large number of images of high variation of radial distortion is generated, which can be well exploited by deep CNN with a high learning capacity. We present quantitative results that demonstrate the ability of our technique to estimate the radial distortion with comparisons against several baseline methods, including an automatic method based on Hough transforms of distorted line images.

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Acknowledgement

This work was supported in part by State Key Development Program Grand No. 2016YFB1001001, NNSFC Grant No. 61322309, and NNSFC Grant No. 61273283.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), School of Electronic Engineering and Computer Science, Peking University, Beijing, 100871, People’s Republic of China

    Jiangpeng Rong, Shiyao Huang, Zeyu Shang & Xianghua Ying

Authors
  1. Jiangpeng Rong
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  2. Shiyao Huang
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  3. Zeyu Shang
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  4. Xianghua Ying
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Corresponding author

Correspondence to Xianghua Ying .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo , Tokyo, Japan

    Yoichi Sato

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Rong, J., Huang, S., Shang, Z., Ying, X. (2017). Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-54187-7_3

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